109 citations · 567 across the 38 of their papers we have counts for
10 papers · 1 filter
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain generalization (DG) aims to learn a generic model from multiple observed source domains that generalizes well to arbitrary unseen target domains without further training. Th…
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
Lulu Zheng, Jiarui Chen, Jianhao Wang +6
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems. In this paper, we introduce a novel…
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
Yinghuan Shi, Jian Zhang, Tong Ling +5
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel…
Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization
Ruiqi Wang, Lei Qi, Yinghuan Shi +1
With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention.…
LibFewShot: A Comprehensive Library for Few-shot Learning
Wenbin Li, Ziyi, Wang +9
Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly sh…
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Qian Yu, Lei Qi, Luping Zhou +5
Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these…
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
Wenbin Li, Xuesong Yang, Meihao Kong +4
Contrastive self-supervised learning (SSL) methods, such as MoCo and SimCLR, have achieved great success in unsupervised visual representation learning. They rely on a large number…
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
Lihe Yang, Wei Zhuo, Lei Qi +2
Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage unlabeled data. In this work, we first construct a strong baseline of self-training (n…
Feature-based Style Randomization for Domain Generalization
Yue Wang, Lei Qi, Yinghuan Shi +1
As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target d…
Mining Latent Classes for Few-shot Segmentation
Lihe Yang, Wei Zhuo, Lei Qi +2
Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e. potential novel clas…